{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入算法包以及数据集\n",
    "from sklearn import neighbors\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "x_data = iris.data[:,:2]\n",
    "y_data = iris.target\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = neighbors.KNeighborsClassifier()\n",
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(model):\n",
    "    # 获取数据值所在的范围\n",
    "    x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "    y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "    # 生成网格矩阵\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                         np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "    z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "    z = z.reshape(xx.shape)\n",
    "    # 等高线图\n",
    "    cs = plt.contourf(xx, yy, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mLlDpnRqHXO50ZWZ3V10CkiISmeWiExPgF4h+Loc/OgZtbVAwYUlnZvBmZ4PWuzUIsz81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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf4876d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7631578947368421"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 画图\n",
    "plot(knn)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "# 准确率\n",
    "knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = tree.DecisionTreeClassifier()\n",
    "dtree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8Y1vjPUvnE6Ii8XrxPT2J7cH3E4+nrS/fKm9OCHu0KjhWRsukLbsbsvQvU0jt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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf4a35c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8157894736842105"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 画图\n",
    "plot(dtree)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "# 准确率\n",
    "dtree.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gPnsKGdsafkDFXKVoKQKEfCnQqPdx1NOOWOeVq06R8cr/vBOuH/mlJuwiSdfn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf34dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7894736842105263"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_knn = BaggingClassifier(knn, n_estimators=100)\n",
    "# 输入数据建立模型\n",
    "bagging_knn.fit(x_train, y_train)\n",
    "plot(bagging_knn)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "bagging_knn.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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beeu33s0XXnj1wvHHz27njm/ewbu/fRt/8eIVNcff9a3b+dVH3sMDh19PMUij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\nfJC6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26ecf43afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7894736842105263"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_tree = BaggingClassifier(dtree, n_estimators=100)\n",
    "# 输入数据建立模型\n",
    "bagging_tree.fit(x_train, y_train)\n",
    "plot(bagging_tree)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()\n",
    "bagging_tree.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
